The (incomplete) SAS Proc GLM output below relates to the analysis of data from a cross-sectional survey. The variable group is categorical (values 1, 2), the variable sex is categorical (0 = F, 1 = M) and variables Y and D are quantitative.   The best calculation of the missing Type III F Value for sex is 0.39 0.73 0.86 1.42 ... View more

This question relates to a SAS Proc GLM analysis of data from a cross-sectional survey looking at the relationship between Y and D (which are quantitative variables). The table below summarises a sequence of fitted models with Y = dependent variable.   Which type of curvature improves the fit of the model the most? D × ln(D) sqrt(D) ln(D) D × D ... View more

The (incomplete) SAS Proc GLM output below relates to the analysis of data from a cross-sectional survey. The variable group is categorical (values 1, 2), the variable sex is categorical (0 = F, 1 = M) and variables Y and D are quantitative.   The best calculation of the missing Type III F Value for sex is 0.39 0.73 0.86 1.42 ... View more

The SAS Proc GLM output below relates to the effect of D (quantitative) on mean Y (quantitative) in males (sex = M) and females (sex = F).   The best calculation of the estimated effect on mean Y of a change in D from 0 to 5 for females is   2.09 0.00 1.14 2.31 ... View more

Th is question relates to a SAS Proc GLM analysis of data from a cross-sectional survey looking at the relationship between Y and D (which are quantitative variables). The table below summarises a sequence of fitted models with Y = dependent variable.   Which type of curvature improves the fit of the model the most? D × ln(D) sqrt(D) ln(D) D × D ... View more